Genetic Vulnerability and Susceptibility
to Substance Dependence
Laura Jean Bierut1,*
1Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
The development of substance dependence requires the initiation of substance use and the conversion from
experimental use to established use before development of dependence. Numerous large twin studies have
indicated a significant genetic contribution to this process. Genetic studies to date have been most success-
ful at identifying genetic factors that influence the transition from regular use to dependence. The availability
of large cohort samples for nicotine and alcohol dependence has resulted in significant progress being made
in understanding at least some of the genetic contributions to these addictions. Fewer studies have repli-
cated specific genetic contributions to illicit drug use, though it is clear that there is a strong genetic compo-
nent involvedhereaswell. Substancedependence canbe thoughtofas apharmacogeneticillness, andmost
likely hundreds and more probably thousands of genetic variants will be required to fully explain the genetic
input to this disease.
Large segments of our population use tobacco, alcohol, and
other drugs. Cigarette smoking is common in both industrialized
and developing countries. In the United States, over 43 million
people use tobacco, and worldwide, over one billion people
are tobacco users (CDC, 2010; WHO, 2010). In the U.S., over
400,000 people die every year from tobacco-related illnesses,
and smoking remains the greatest contributor to preventable
death (Mokdad et al., 2004). With increasing tobacco use in
developing countries, it is predicted that the worldwide death
toll will rise to eight million people per year by 2030. Alcohol is
the most commonly used and abused substance in the popula-
tion, and 12.5% of adults in the U.S. develop alcohol depen-
dence during their lifetime (Hasin et al., 2007). In 2004, the World
Health Organization (WHO) estimated that alcohol use disorders
affected 76.3 million people globally (WHO, 2004). In the U.S.,
almost 80,000 people die per year from the consequences of
alcohol consumption, which includes alcohol-related illnesses
and accidents (Mokdad et al., 2004). Our society pays a high
price for substance use, primarily through increased health
care costs and judicial system expenditures. It is estimated
that over 11% of federal and state government budgets ($374
billion in 2005) are spent dealing with the consequences of
tobacco, alcohol, and other substance use, abuse, and depen-
dence (The National Center on Addiction and Substance Abuse
at Columbia University, 2009).
The development of addiction requires the use of a substance
and a subsequent chain of behavioral events that leads to addic-
tion. The key steps in the development of addiction include the
initiation of substance use and the conversion from experimental
use to established use before the actual development of addic-
tion (see Figure 1). Each step is influenced by environmental and
genetic factors, some of which are common to all steps, and
others that are specific. For example, environmental factors,
such as the availability of nicotine, alcohol, and drugs, play
a role in each stage in the development of addiction, but acces-
sibility of a substance is relatively more important in the initiation
of substance use. Similarly, high cost of a substance through
taxationcan reduceinitiation,use,andaddiction; however,taxa-
tion has a stronger influence on teenagers who have less money,
thus limiting initial use. Family, twin, and adoption studies also
convincingly demonstrate a substantial genetic contribution to
the development of addiction to nicotine, alcohol, and illicit
drugs. Heritability estimates for nicotine, alcohol, and drug
addiction are in the range of 50% to 60% (Heath et al., 1997;
Tsuang et al., 1998; Kendler et al., 2003; Li, 2006). In general, it
appears that environmental factors have a stronger effect on
initiation, whereas genetic factors play a larger role in the transi-
tion from regular use to the development of addiction (Vink et al.,
2005). Given the robust behavioral evidence for the role of
genetic influence in addiction, genetic studies are warranted.
Initial inroads into understanding the genetic influences of
addiction in humans relied on both genetic linkage mapping
and candidate gene association studies, resulting in the identifi-
cation of hundreds of potential genes contributing to the addic-
in independent studies, potentially reflecting a number of false
positives and/or genetic heterogeneity in which multiple genes
contribute modest effects. The last decade, however, has seen
a revolution in genetic technologies, and now hundreds of
thousands of genetic variants (or single nucleotide polymor-
phisms; SNPs) can be queried in thousands of individuals in
a cost-effective manner. This technology facilitates genome-
wide association studies (GWAS) that test for an association of
genetic variants with an illness in order to discover genetic
contributions to complex diseases. Complex diseases are
caused by many genetic and environmental factors working
together, and GWAS has permitted the discovery of hundreds
of genetic variants that alter the risk of developing multiple
complex diseases, including type 2 diabetes, Crohn’s disease,
and Parkinson’s disease (Hindorff et al., 2010). More recently,
the genetic tools of GWAS have been applied to the study of
Neuron 69, February 24, 2011 ª2011 Elsevier Inc.
addiction to identify genetic variations that contribute to this
illness. The success of this approach has been in part due
to the creation of genetic research consortia for the study of
nicotine and illicit drugs (NIDA Genetics Consortium; http://
html) and alcohol (e.g., NIAAA’s Collaborative Study on the
Genetics of Alcoholism [COGA]; http://www.niaaa.nih.gov/
projcoga.htm), permitting the collection of the massive numbers
of comprehensively assessed subjects and DNA samples
with the scientific community through the database of Geno-
types and Phenotypes (dbGaP; http://www.ncbi.nlm.nih.gov/
about the genetic underpinnings of addiction.
This review will give a synopsis of the current understanding of
genetic contributions to the vulnerability of substance depen-
dence. There have been extensive discussions about the termi-
nology used to define substance use disorder—‘‘dependence’’
versus ‘‘addiction.’’ Substance dependence is the official diag-
nostic nomenclature used in the fourth edition of the Diagnostic
and Statistical Manual of Mental Disorders (DSM-IV; APA, 1994)
to represent the syndrome of substance misuse that leads to
adverse consequences and includes a cluster of symptoms
such as tolerance, withdrawal, and inability to stop using (see
DSM-IV substance dependence for the complete diagnostic
criteria). The creators of the DSM-IV criteria selected the term
‘‘dependence’’ because of the concern of stigmatization associ-
ated with ‘‘addiction.’’ At this time, revisions to the fifth edition of
the Diagnostic and Statistical Manual of Mental Disorders
(DSM-V) are underway for release in 2013. In this revision, issues
have again been raised about the term used to define this clinical
syndrome. In order to differentiate from the normal physiologic
development of tolerance and withdrawal that develops with
DSM-V proposes the use of the word ‘‘addiction’’ to define
substance use disorder. The words ‘‘dependence’’ and ‘‘addic-
tion’’ are used interchangeably in this review to represent the
same underlying concept of substance use disorder.
Genome-Wide Association Studies of Nicotine
The strongest genetic contribution to nicotine dependence
comes from variation in the nicotinic receptor subunits, and the
most compelling genetic evidence is provided by several large-
scale GWAS meta-analyses of smoking behavior (Liu et al.,
2010; Thorgeirsson et al., 2010; TAG Consortium, 2010).
Because smoking is a major contributor to many illnesses, ciga-
rettes smoked per day (CPD), a proxy phenotype for nicotine
dependence, has been measured in many genetic studies, and
this has allowed meta-analyses of over 80,000 individuals of
European ancestry. These genetic meta-analyses of CPD
confirm that two chromosomal regions containing nicotinic
receptor subunit gene clusters influence smoking behavior.
The most robust genetic finding that alters the risk of
developing heavy smoking is in the chromosome 15q25 region,
which contains the a5, a3, and b4 nicotinic receptor subunit
gene cluster (CHRNA5, CHRNA3, and CHRNB4). The SNP
rs16969968 is unequivocally associated with smoking behavior
(p = 4.48 3 10?33and p = 5.57 3 10?72in combined analyses)
(Figure 2; TAG Consortium, 2010). Further examination of the
chromosome 15 region demonstrates that there are at least
two distinct genetic risk variants that contribute to heavy
smoking behavior (Saccone et al., 2010a; TAG Consortium,
Variation in anindependent group of nicotinic receptors is also
associated with the development of heavy smoking and nicotine
dependence. The nicotinic receptor gene cluster on chromo-
some 8 that includes the a6 and b3 nicotinic receptor subunit
gene cluster (CHRNA6, CHRNB3) is correlated with smoking
behavior. This region generated genome-wide significant asso-
ciation with nicotine dependence, though the strength of this
Figure 1. Steps in the Development of Dependence
Figure 2. Genome-Wide Association Results for Cigarettes Per Day
Manhattan plot, indicating significance of association of all SNPs in the TAG
Consortium meta-analysis for cigarettes per day. Manhattan plot shows
SNPs plotted on the x axis according to their position on each chromosome,
and plotting on the y axis is shown as negative log10p value. Chromosome
15 contains the strongest genetic contribution to the risk of developing nico-
tine dependence. Figure courtesy of TAG Consortium (2010).
Neuron 69, February 24, 2011 ª2011 Elsevier Inc.
association is much less (rs6474412 p = 1.4 3 10?8) (Thorgeirs-
son et al., 2010).
uting to the development of nicotine dependence, genetic varia-
tion in nicotine metabolism plays an important role in cigarette
consumption (Schoedel et al., 2004; Minematsu et al., 2006)
and nicotine dependence (Audrain-McGovern et al., 2007).
Conversion of nicotine to cotinine accounts for 70% of initial
nicotine metabolism and is performed by the CYP2a6 enzyme
(Yamazaki et al., 1999; Su et al., 2000; Malaiyandi et al., 2006).
Important functional polymorphisms of CYP2a6 include large
deletions and gene recombinations that involve neighboring
genes (Oscarson et al., 1999, 2002). The importance of nicotine
metabolism and variation in the CYP2a6 region on chromosome
19 was recently reinforced by the GWAS meta-analysis studies
in which variants in this region were associated with number of
CPD (Thorgeirsson et al., 2010; TAG Consortium, 2010). The
most significant SNP reported in this region, the intergenic
variant rs41405144, lies within two large deletions (defined
as CYP2a6*4 and CYP2a6*12). This variant, rs41405144, is
correlated with rs1801272, a nonsynonymous SNP that defines
the CYP2a6*2 loss-of-function allele. These findings confirm
that variation in nicotine metabolism contributes to the number
of cigarettes smoked daily and the development of nicotine
Genetics of Alcohol Dependence
Alcohol dependence was one of the first behavioral disorders
shown to have validated genetic contributions. Polymorphisms
in the alcohol metabolizing enzymes are the most strongly asso-
ciated genetic variants that influence alcohol consumption and
alcohol dependence. In 1972, individuals of Asian descent
were reported to have facial flushing and decreased tolerance
when exposed to alcohol (Wolff, 1972). The flushing reaction
after ingesting alcohol is secondary to a deficiency of aldehyde
dehydrogenase (specifically ALDH2), an enzyme involved in
the metabolism of ethanol (Goedde et al., 1980). The ALDH2
deficiency was found to be present in a large part of the general
Japanese population, but uncommonin alcohol-dependent indi-
viduals, implying a protective role for the deficiency of ALDH2 in
alcohol dependence (Harada et al., 1982).
Since these initial discoveries, much has been learned about
alcohol metabolism. Ethanol metabolism occurs predominantly
in the liver in two steps: the oxidation of ethanol to acetaldehyde
catalyzed by alcohol dehydrogenases (ADHs), and the oxidation
of acetaldehyde to acetate by acetaldehyde dehydrogenases
(ALDHs). Several known genetic variants cause amino acid
changes in these proteins and alter enzymatic activity. For
instance, ADH1B*2, or rs1229984, diminishes ADH1b enzymatic
activity several-fold, and ALDH2*2, or rs671, results in a nearly
inactive enzyme (Edenberg, 2007). These genetic variants
reduce the probability of heavy alcohol consumption and the
development of alcohol dependence (Edenberg, 2007; Macgre-
gor et al., 2009; Sherva et al., 2009). The mechanism by which
variants of these enzymes influence the risk of developing
alcohol dependence is hypothesized to be through an elevation
of acetaldehyde levels after drinking, leading to facial flushing,
nausea, and other adverse reactions.
In terms of GWAS assessments, in contrast to the GWAS of
smoking behaviors to date, GWAS of alcohol dependence
have been less consistent in identifying genetic variants associ-
ated with alcoholism (Treutlein et al., 2009; Bierut et al., 2010;
Edenberg et al., 2010). One main reason for the differences in
results is that these initial studies of alcohol dependence are of
modest size by GWAS standards, with only a few thousand
subjects compared with the tens of thousands of subjects in
the GWAS of smoking behaviors. Each study identified novel
regions that have suggestive evidence of association with
alcohol dependence, including PECR (Treutlein et al., 2009), an
enzyme involved in fatty acid metabolism; PKNOX2 (Bierut
and death; and SLC22A18 (Edenberg et al., 2010), a solute
carrier. However, there is not consistent replication across
studies. In addition, the alcohol metabolizing genes previously
found to be associated with alcohol dependence are not well
queried on the genetic platforms used by these studies, so
they remain to be validated by GWAS. For instance, rs1229984
and rs671 are not genotyped on many of the initial GWAS chips
(www.broadinstitute.org/mpg/snap; Johnson et al., 2008). Over-
butions to alcohol dependence will be of modest effect. Larger-
scale meta-analyses are underway, and hopefully these studies
will discover unique genetic associations with alcoholism.
While alcoholism GWAS await further validation, some of
the candidates coming out of these earlier human genetic
approaches have support from work in animal model systems,
and therefore seem like potentially stronger alcoholism risk
candidate genes. Animal models support the human genetic
studies implicating the g-aminobutyric acid (GABA) system as
fundamentally involved in alcohol intoxication and withdrawal
and other behavioral aspects of alcoholism (Krystal et al.,
2006). Ethanol enhances GABAAreceptor function (Bowen and
Grant, 1998) and electrophysiologic studies implicate GABAA
receptors as targets for the effect of ethanol in the central
nervous system (Suzdak et al., 1986). Multiple candidate gene
reports show an association between variants in GABRA2 and
alcohol dependence (Enoch, 2008). In a hypothesis-driven
approach to test this association as part of a GWAS, we find
a modest association with GABRA2 (Odds Ratio = 1.1 and
p z 0.01), further supporting the role of this candidate gene in
the development of alcoholism.
Genetic Influences for Other Drug Addictions
Though less common in the general population, illicit addictions
such as cocaine and opiate dependence can be more devas-
tating socially, cause more physical illnesses, and represent an
extreme of addiction. Because illicit drug addiction is less
common, large-scale GWAS have not been undertaken as yet.
Instead, the approach to studying the genetics of drug addiction
has been through candidate genes. Hundreds of candidate gene
association studies for drug addiction as well as for nicotine and
alcohol dependence had been performed in the pre-GWAS era.
Thousands more candidate gene studies have been undertaken
for other medical illnesses. However, a disconnect between
these reported candidate gene associations and the findings
from GWAS exists in both the addiction field and across all of
Neuron 69, February 24, 2011 ª2011 Elsevier Inc.
medicine. If these candidate gene studies are valid, then the
GWAS should identify thousands of genetic variants that play
a role in disease. Though hundreds of genetic variants have
been conclusively confirmed by GWAS as contributors to
complex diseases, the number of confirmed genetic variants is
more modest than what is expected from the candidate gene
studies. Overall, only a modest percentage of the numerous
genetic associations proposed in the candidate gene era have
been subsequently replicated in GWAS (Siontis et al., 2010),
which suggests that many of the candidate gene studies con-
tained false positive reports. Interestingly, those that conclu-
sively replicate have strong genetic effects.
This lack of replication across methods reflects two distinct
issues in these different study designs: the low threshold for
significance in candidate gene studies results in a high false
positive rate, and the high threshold for significance in the
GWAS design leads to a low sensitivity to true genetic contribu-
tions to disease. While candidate gene studies of addiction
should therefore be interpreted with caution, they should not
be dismissed, because they may have captured unique pheno-
types of genetic variation that will not be seen in the large-scale
heterogeneous GWAS. Regardless, validation of human genetic
mutations linked to illicit drug use awaits further study.
In the interim, animal models of addiction continue to provide
insights into potential candidate genes that would benefit from
more directed study in humans. A number of these studies
have targeted the known neurobiological systems regulating
the dopamine reward system and the endogenous opioid
system. Dopamine plays a key role in reward behavior, yet the
association with alcoholism and other drugs remains controver-
sial. There are equally prominent association studies of DRD2
with alcoholism and other drug addictions and failures to repli-
cate (Gelernter et al., 1991; Parsian et al., 1991, Le Foll et al.,
2009). Similarly, the endogenous opioid system clearly plays
a role in addiction, and an amino acid change in the m opioid
receptor (OPRM1) displays functional changes with up to
3-fold variation in the affinity of the receptor to bind beta-endor-
phin, the endogenous opioid (Bond et al., 1998). However,
a large-scale meta-analysis does not demonstrate that this
variant alters the risk of developing addiction (Arias et al., 2006).
We have the tools in hand now to directly test many of these
candidate genes in large-scale studies using uniform criteria
for diagnosis and outcomes along with tools to genotype the
specific variant needed, and so the contributions and contro-
versy of these potential associations will be resolved in the
Common and Specific Factors in Addiction
The above sections have focused on candidate genes for
specific addictions (summarized in Table 1), and several of the
confirmed genetic findings support that specific genetic variants
contribute to specific substance dependence risk. For instance,
variants in the alcohol metabolizing genes specifically contribute
to differences in alcohol consumption and alcohol dependence,
but not to other addictive behaviors. Similarly, the variation in
nicotine metabolizing genes contributes to smoking behavior
and CPD, but not alcoholism or other drug addiction. Yet data
from family and twin analyses also support the idea that there
is a strong contribution from common genetic factors to the
development of dependence on various classes of drugs (Bierut
et al., 1998; Merikangas et al., 1998; Tsuang et al., 1998; Kendler
et al., 2003). In fact, twin studies have convincingly shown that
most of the genetic variation to addiction is shared across the
liability to develop nicotine, alcohol, and illicit drug addiction
(Tsuang et al., 1998; Kendler et al., 2003). As a result, once an
association is identified, the next step is to test whether this
genetic variant influences multiple drug dependencies.
The chromosome 15 variant in the a5 nicotinic receptor,
rs16969968, which influences the development of nicotine
dependence, has also been independently shown to contribute
to the occurrence of alcohol and cocaine dependence. The
minor allele of rs16969968 that is correlated with an increased
risk for nicotine dependence is associated with a decreased
risk for alcohol and cocaine dependence (Grucza et al.,
2008; Chen et al., 2009; Sherva et al., 2010). This bidirectional
association is hypothesized to be due to the involvement of
nicotinic receptors with both excitatory and inhibitory modula-
tion of dopamine-medicated reward pathways. These data
reinforce the importance of variation in the CHRNA5-CHRNA3-
CHRNB3 gene cluster for risk of dependence on multiple
substances, although the direction of the effects varies
across substances. In addition, variants in this region influence
the initial responses to alcohol and nicotine in adolescents
(Schlaepfer et al., 2008).
As we identify other genetic variants associated with addic-
tion, it will therefore behoove us to test the potential contribution
of each variant across the wide range of abused substances, as
it is likely that some variants will be common risk factors of rele-
vance to multiple addictive substances.
Where Is the Unexplained Variance?
Though the GWAS-based approach has been successful for
investigating the genetic influences of nicotine dependence
ance remains unexplained (Frazer et al., 2009). The heritability of
addiction is approximately 50%, yet the confirmed genetic
contributions to nicotine dependence (through the nicotinic
receptors and nicotine metabolizing genes) and alcohol depen-
dence (through alcohol metabolizing genes) explain only a small
fraction of this heritability. There are two main explanations for
the missing variance: rare variation not queried on the current
GWAS chips, and many genes of small effect. It is likely that
both of these contribute to the missing genetic variance.
Clearly, part of this missing variance is related to coverage of
the existing GWAS chips. By design, GWAS test for association
Table 1. Genes and Proteins Associated with Addiction
Dependence Gene or Protein
Neuron 69, February 24, 2011 ª2011 Elsevier Inc.
with common variants (allele frequencies >5%). The less
common (or ‘‘rare’’ variants with allele frequencies <5%) are
not adequately represented on the existing arrays. For example,
the well-known genetic variants that alter alcohol metabolism,
rs1229984 in ADH1b and rs671 in ALDH2, are not queried on
most of the commercial GWAS chips. Although individually
rare, these variants are collectively frequent, and their contribu-
tion to disease can be greater than those observed for common
variants (Bodmer and Bonilla, 2008). Several other rare mecha-
nisms can contribute to the modest explanation of variance to
date. Structural variants, which include insertions and deletions,
inversions, and translocations, can account for some of the
unexplained heritability. Sequencing will be needed to allow us
to definitively detect and test this class of variation.
Yet, there is also evidence that multiple common variants can
begin to explain more of genetic variation in addiction. For
smoking behavior, for example, we know that individual genetic
variants contribute only a small effect to the development of
nicotine dependence. Yet in combination, these genetic factors
play a substantial role in the development of heavy smoking. For
example, in our study from the Collaborative Genetic Study of
Nicotine Dependence (COGEND), approximately nine variants
in the nicotinic receptors explain 5% of the phenotypic variance
ance estimate is likely higher than what will be seen in a general
population study of smoking behavior, it demonstrates that
collectively common genetic polymorphisms of small effect
can begin to explain a larger proportion of genetic variation
related to disease. Most likely hundreds, and more probably
thousands, of genetic variants will be required to explain the
genetic input to disease.
An additional potential drawback to GWAS is that there is
heterogeneity of study design that may obscure true genetic
contributors to disease, and careful consideration in the design
of future addiction GWAS may help to alleviate this issue. An
example is seen in the comparison of our study (COGEND),
designed to examine genetic influences on smoking behavior
(Saccone et al., 2009, 2010b; Thorgeirsson et al., 2010). Our
COGEND study compared very light smokers and current nico-
tine-dependent smokers, thus focusing on differences between
those who can smoke a little and not become addicted and
individuals with addiction. In addition, our sample recruited
subjects using a systematic strategy and in a relatively narrow
age range (25–44) to avoid the confounding of secular trends in
smoking. Conversely, the large-scale GWAS of smoking were
based on current and former smokers, and the entire range of
smoking amount was included. The age range in these studies
encompassed different generations in which we know smoking
for lung cancer, others for heart disease, and others for many
other medical illnesses.
Recent meta-analyses have suggested that our more focused
study design has paid off—our ascertained sample that included
a narrow age range and specific smoking behavior requirements
increased power to detect genetic variation compared with a
more heterogeneous GWAS. Two of the top genetic findings—
rs16969968 in CHRNA5 and rs6474412 in CHRNB3—showed
significance levels of 5.57 3 10?72with a sample size of n =
73,853 (TAG Consortium, 2010) and 1.4 3 10?8with a sample
size of n = 84,956 (Thorgeirsson et al., 2010). In our COGEND
sample of 2062 subjects of European descent, we have a
significance level of 4 3 10?7for the CHRNA5 variant and
1.37 3 10?3for the variant in CHRNB3 (Saccone et al., 2010a,
2010b), representing a 3-fold and 10-fold increase, respectively,
in the power to detect genetic variation compared with a more
heterogeneous GWAS. These comparisons demonstrate the
amplified power of a study design through the systematic
ascertainment, targeted age range, and phenotypic contrast of
lifetime light smokers versus current heavy smokers.
From Genetic Association to Function
The above sections have highlighted how human genetic tools
have aided in the identification of genetic variants contributing
to the addiction cycle. Yet it needs to be understood that
a genetic association characterizes only the first stage in under-
standing the underlying biology that leads to disease. A genetic
association represents not only an association with tested
genetic variants, but also an association with untested, highly
correlated SNPs that can span across many genes on the
same chromosome. A challenge once a genetic association is
confirmed is to then understand which of these variants
contribute to the biological mechanism underlying the correla-
tion with a disease.
In the chromosome 15 region, the most biologically credible
variant associated with nicotine dependence is rs16969968,
apolymorphism thatcausesanaminoacid changefromaspartic
acid to asparagine (Asp398Asn) in the a5 nicotine receptor
subunit. Several lines of evidence point to this variant as
having functional importance. The specific region in the a5
protein that includes this polymorphism is highly conserved
across different species, which implies biological importance
(aspartic acid is conserved in chimpanzee, Bolivian squirrel
monkey, domestic cow, mouse, chicken, and African clawed
frog) (Bierut et al., 2008). An in vitro functional study found that
(a4b2)2a5 receptors that only differed by the asparagine amino
acid substitution exhibited altered response to a nicotine agonist
compared with receptors containing the aspartic acid amino
acetylcholine receptor lowers Ca2+permeability and increases
short-term desensitization in (a4b2)2a5, but does not alter the
receptor sensitivity to activation (Kuryatov et al., 2011). The
high sensitivity to activation and desensitization of (a4b2)2a5
nicotine acetylcholine receptors by nicotine results in a narrow
concentration range in which activation and desensitization
curves overlap at nicotine concentrations typically sustained
in smokers. It is predicted that smokers would desensitize
most of these receptors while permitting a smoldering activation
of the remainder of the receptors. In addition, the a5 nicotinic
receptor subunit is expressed in the brain regions that are
important in the pathways relevant to the development of
dependence. Finally, this key a5 gene variant is associated
with a dorsal anterior cingulate-ventral striatum/extended amyg-
dala circuit, and the ‘‘risk allele’’ decreases the intrinsic resting
functional connectivity strength in this circuit (Hong et al.,
Neuron 69, February 24, 2011 ª2011 Elsevier Inc.
2010). Importantly, this effect is observed in nonsmokers and it
appears to represent a trait circuitry biomarker.
In the chromosome 15 region, the second independent
genetic association with nicotine dependence is marked by
rs880395 (Saccone et al.,2010a), and functional studies suggest
a distinct biological mechanism: altered a5 nicotinic receptor
mRNA expression (Wang et al., 2009; Falvella et al., 2010; Smith
et al., 2011). Variants tagged by rs880395, which are more than
sion of CHRNA5 mRNA is correlated with an increased risk of
heavy smoking and nicotine dependence (Wang et al., 2009).
This change in expression is not seen in lymphocytes, which
demonstrates that genetic variants can have tissue-specific
biologic effects (Smith et al., 2011).
These findings of the a5 nicotinic receptor in humans have
motivated further animal studies of this receptor subunit, which
show that the habenulo-interpeduncluar pathway is a key neuro-
circuit acts as a negative feedback response, opposite to the
mesoaccumbens positive reward pathway. This animal work
suggests that individuals with the a5 nicotinic receptor risk
alleles for nicotine dependence are relatively insensitive to the
inhibitory effects in the reward pathway. This type of work—
spanning humans, other animals, individual cells, and then
back to humans—represents the power of genetic studies. We
can identify associations, target new genes for study, and then
test hypotheses in both other animals and humans.
These genetic associations with nicotine and alcohol depen-
dence and these proposed mechanisms of biologic action
including neurotransmission and metabolism provide new
insights into the underlying biology associated with addiction.
Identifying how specific variants and genes associated with
addictive behavior affect brain function will be key to under-
standing the development of dependence; yet, numerous ques-
tions remain. For example, will these mechanisms of action
associated with genetic variation be expressed in all regions of
the brain, or will the genetic effect be region specific? Will these
variants have a similar influence throughout the lifespan, or will
there be critical periods when these genetic variations alter the
risk of developing addiction? Though these biological mecha-
nisms are proposed to lead to the altered risk for the develop-
ment of addiction, they represent but an initial understanding
of the mechanisms of dependence, and it is likely that there
willbemorecomplex biologic functionsunderlying thesegenetic
Convergence of Genetic Findings of Addiction
There is an intriguing convergence of genetic findings for nico-
tine and alcohol dependence and medical disorders. Smoking
is the strongest risk factor for the development of lung cancer
andchronic obstructive pulmonary disease (COPD). Large-scale
genetic studies demonstrate that the same variants on chromo-
some 15 that are associated with smoking behavior are also
the strongest genetic risk factors for lung cancer and COPD
(Amos et al., 2008, 2010; Hung et al., 2008; Liu et al., 2008;
Thorgeirsson et al., 2008; Broderick et al., 2009; Pillai et al.,
2009; Shiraishi et al., 2009; Lips et al., 2010). The convergence
of these genetic findings associated with smoking behavior
and smoking-related illnesses raises the question of whether
this locus has a direct biologic effect on the risk of developing
lung cancer and COPD, or if the increased genetic risk of lung
cancer and COPD can be explained solely through the genetic
influences on smoking behavior.
The data remain mixed as to whether the genetic risk on chro-
mosome 15 and lung cancer and COPD is related to heavier
smoking (an indirect effect) or whether a direct biological mech-
anism increases lung cancer and COPD risk independent of
smoking (a direct effect). Evidence in favor of a direct biological
effect is that this genetic risk for lung cancer and COPD associ-
ation with thesevariants remains afterstatistically accounting for
duration of smoking history and number of CPD (Lips et al.,
2010). The a5 nicotinic receptor subunit is expressed in lung
tissue, and a 30-fold upregulation of expression of CHRNA5 is
seen in lung cancer tissue compared with normal lung tissue
(Falvella et al., 2010).
On theother hand,thischromosomal region doesnotincrease
the risk of lung cancer among nonsmokers (Lips et al., 2010).
Furthermore, CPD may not fully account for the exposure to
carcinogens in cigarette smoke. An intriguing study demon-
strated that smokers with the risk variants in the chromosome
15 region ingested more toxins even after controlling for the
number of cigarettes smoked (Le Marchand et al., 2008). This
implies that the smokers with the risk variants are inhaling
more intensely and increasing their exposure to nicotine and
other carcinogens in cigarette smoke. Thus the measurement
of CPD is an imprecise measure of the risk of smoking related
to lung cancer and COPD.
A parallel finding is seen with genetic variants that influence
alcohol consumption, alcohol dependence, and esophageal
cancer. Large studies of esophageal cancer, a cancer related
to alcohol use, identify two genetic variants in alcohol metabo-
lizing genes that influence alcohol consumption and alcohol
dependence (ADH1b variant rs1229984, and ALDH2 variant
rs671) and also contribute to the risk of esophageal cancer
(Hashibe et al., 2008; Tanaka et al., 2010). Even after controlling
for alcohol consumption in the analyses, the protective effects of
these variants for esophageal cancer remain strong. This implies
that variants in alcohol metabolizing genes not only reduce
alcohol consumption and decrease the risk for alcohol depen-
dence, but also lower the susceptibility to esophageal cancer,
perhaps by reducing the carcinogenic effects of alcohol, its
metabolites, and other toxins.
Both of these examples challenge paradigms about the rela-
tionship between addiction and cancer. Epidemiologic data
clearly support the association of addiction with cancer:
smoking and nicotine dependence are associated with lung
cancer; and alcohol consumption and alcohol dependence are
associated with esophageal cancer. As a result, exposure to
smoking and alcohol has been considered an environmental
variable to be controlled in the study of cancer. However, the
strongest genetic findings for the development of addiction are
also the strongest genetic predictors for the correlated cancers.
These findings blur the distinction between genetic and environ-
mental risks with nicotine and alcohol addiction. It also remains
Neuron 69, February 24, 2011 ª2011 Elsevier Inc.
genetic variants can be completely explained through addictive
behaviors, or if biologic mechanisms act in the brain to increase
the risk of addiction while also acting in the lung and esophagus
be able to separate the genetic influence of these variants on the
development of dependence fromthe genetic contribution to the
development of cancer.
Genetic Implications for Different World Populations
Although dependence iscommon in all populations, to date all of
the large-scale GWAS have been performed in populations of
European descent. Though the underlying biological mecha-
nisms that lead to the development of substance dependence
are most likely the same across populations, varying allele
frequencies can alter the relative importance of specific genetic
risk factors in different populations. For example, the variant
rs16969968, which is relatively frequent in populations of Euro-
pean descent (37% allele frequency), is rare in populations of
African or Asian descent (0% to 3% allele frequency) (Bierut
et al., 2008). Similarly, the polymorphisms that cause amino
acid changes in alcohol metabolizing genes, rs671 and
rs1229984, are common in Asian populations, but are rare in
populations of European and African descent (Edenberg,
2007). Thus, rs16969968 will play a larger role in the develop-
ment of heavy smoking and nicotine dependence in populations
of European ancestry compared with populations of African and
influence alcohol consumption and alcohol dependence in Asian
new genetic frontier is to leverage these differences in genetic
architecture across populations to refine association signals
and narrow down associations to the most likely biologically
causative variants. This strategy highlights the importance of
recruiting, assessing, and studying diverse populations.
Future of Genetic Studies
As we are beginning to understand some of the genetic factors
that alter our individual vulnerability to dependence, the future
of geneticstudieshas thepotential topersonalize our treatments
for addiction. We have unequivocal evidence of genetic variation
that contributes to the development of this behavioral disorder.
Addiction represents a great success in psychiatric genetics.
The strongest specific genetic contributors to dependence are
related to the pharmacologic responses to nicotine and alcohol
olizing genes, and alcohol metabolizing genes.
Though some might say that GWAS have failed because we
this represents a very narrow view of the field. We have convinc-
ingly identified genetic variantsthatcontribute to addiction. Ifthe
progress in other medical disorders can be used as an example,
sands and potentially hundreds of thousands of people will soon
discovernew variantsthatcontribute to addiction.Wenowknow
that the genetic risk is modest (OR 1.3 or less) for variants that
are common in the population, but rarer variants may have
represents the combination of hundreds or thousands of genes
of modest effect.
Weareat astage wherewecan take several productive paths.
First, we must integrate the results from candidate gene studies
with the findings from GWAS. By synthesizing both approaches
into a cohesive model, we will be able to balance the high false
positive rate in candidate gene studies with the high false nega-
tive rate in GWAS. This will allow us to separate the wheat from
more variants from the GWAS approach. Second, in genetic
studies, ascertainment and phenotypes matter and size is not
everything. Though we have thrown together GWAS from
many different fields, it is time to go back and more carefully
select studies for inclusion so that similar ascertainments can
be used to reduce heterogeneity. An improvement of pheno-
types should also aid in the discovery of genes. For example,
CPD is an effective, but imprecise, measurement of nicotine
addiction. Though a large sample size can overcome a crude
measure, there is a gain of power with more exact assessments.
A balance must be reached between the smaller sample sizes in
genetic studies with comprehensive assessments and large
samples with simpler phenotypes.
Tools areunder development to aidscientists, physicians,and
the public in the synthesis, interpretation, and dissemination of
findings in human genetic variation in health and disease. One
mechanism is the Human Genome Epidemiology Network
Since 2001, HuGENet has maintained a searchable database
of published, population-based epidemiologic studies of human
genes extracted and curated from PubMed. This website allows
the user to search by disease and gene with the goal of aiding in
the translation and integration of genomics into public health
research, policy, and practice.
of the genetics of addiction is to ultimately improve our care for
individuals with this disorder. Our current treatments for alcohol
and nicotine dependence are related to the pharmacologic
response of these substances. For example, we exploit the aver-
feres with ALDH, and thus increases acetaldehyde levels when
alcohol is ingested. This build up of acetaldehyde causes symp-
toms of nausea, vomiting, flushing, and headache, and is similar
to the biologic response seen in individuals who carry an alcohol
metabolizinggenedeficiency.Wemaybeableto utilizethe varia-
tion in nicotinic receptors and nicotine metabolizing genes to
improve our treatments for smoking. As we begin to understand
more of the genetic diversity that influences an individual’s
specific risk of dependence, we will highlight new biologic path-
ways and neural circuitry that may be exploited pharmacologi-
cally. By identifying genetic risks that contribute to dependence,
wecanbeginto dissectdifferentcontributionsof genesandenvi-
ronments that lead to dependence, and in turn we can improve
interventions to reduce dependence and improve cessation.
This work was supported by grants from the National Institute on Drug
Abuse (K02 DA021237, R01 DA019963), the National Cancer Institute
Neuron 69, February 24, 2011 ª2011 Elsevier Inc.
(P01 CA089392), and the National Institute on Alcohol Abuse and Alcoholism
(U10 AA008401). The assistance of Sherri Fisher was key in the preparation
of this manuscript. Dr. Bierut is listed as an inventor on a patent, ‘‘Markers
of Addiction,’’ covering the use of certain SNPs in diagnosing, prognosing,
and treating addiction. Dr. Bierut served as a consultant to Pfizer in 2008.
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